Artificial Intelligence and Hand Hygiene Accuracy: A New Era in Infection Control for Dental Practices by Salwa A. Aldahlawi, Amr H. Almoallim, Ibtesam K. Afifi

“…Future research should explore the application of AI technology in different dental settings to further validate its feasibility and adaptability.…”
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Edge-Enhanced GCIFFNet: A Multiclass Semantic Segmentation Network Based on Edge Enhancement and Multiscale Attention Mechanism by Long Chen, Zhiyuan Qu, Yao Zhang, Jingyang Liu, Ruwen Wang, Dezheng Zhang

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Detecting stress parameters in dromedary camels using computer vision by Hiba Moideen, Manar Abu Talib, Nabil Mansour, Shaher Bano Mirza, Ali Bou Nassif, Simon Zerisenay Ghebremeskel, Fouad Lamghari, Yaman Afadar, Takua Mokhamed

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IOT-MDEDTL: IoT Malware Detection based on Ensemble Deep Transfer Learning by Qasim Kadhim, Ahmed Qassem Ali Sharhan Al-Sudani, Inas Amjed Almani, Tawfeeq Alghazali, Hasan Khalid Dabis, Atheer Taha Mohammed, Saad Ghazi Talib, Rawnaq Adnan Mahmood, Zahraa Tariq Sahi, Yaqeen Mezaal

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Improving robotic grasping accuracy through oriented bounding box detection with YOLOv11-OBB by Vo Duy Cong, Le Hoai Phuong

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GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification by Junding Sun, Haoxiang Dong, Yanlong Gao, Xiaosheng Wu, Jianlong Wang, Yudong Zhang

“…First, a grouped multiscale extraction module captures and fuses spectral features at different scales using various kernels. Second, a grouped multiangle convolution module extracts features from multiple directions, with an adaptive fusion module further integrating this information. …”
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Deep Learning-Enabled Dynamic Model for Nutrient Status Detection of Aquaponically Grown Plants by Mohamed Farag Taha, Hanping Mao, Samar Mousa, Lei Zhou, Yafei Wang, Gamal Elmasry, Salim Al-Rejaie, Abdallah Elshawadfy Elwakeel, Yazhou Wei, Zhengjun Qiu

“…The proposed approach was validated using extensive sequential hyperspectral reflectance measurements acquired from lettuce leaves at different growth stages across the growing season. …”
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PEMETAAN PENGGUNAAN LAHAN SAWAH BERDASARKAN PENDEKATAN EKOLOGI BENTANG LAHANMENGGUNAKAN CITRA PEREKAMAN TUNGGAL by Algi Variski Hasibuan, Projo Danoedoro, Sigit Heru Murti

“…Such maps are usually created using multitemporal image data with a spectral approach, but this method can only be applied to certain areas and cannot be easily applied to other areas with different land characteristics. While multitemporal data has been widely used by researchers and proven effective, using single-date imagery can be more efficient. …”
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Facial Beauty Prediction Combining Dual-Branch Feature Fusion With a Stacked Broad Learning System by Junying Gan, Hantian Chen, Wenchao Xu, Huicong Li, Zhenxin Zhuang, Zhen Chen

“…Stacked BLS enhancing the network’s ability to perceive features at different levels. This model is named SDB-BLS (stacked dual branch broad learning system). …”
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Unsupervised hyperspectral noise estimation and restoration via interband-invariant representation learning by Zhaozhi Luo, Janne Heiskanen, Xinyu Wang, Yanfei Zhong, Petri Pellikka

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An efficient network for object detection in scale-imbalanced remote sensing images by Li Zhongyu, Jing Xiaoping, Sun Rui, Yang Yazhi, Wang Wei, Zhu Hongzhen

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New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform by Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

“…Segmenting and transforming different PWAS signals into image-based data points led to data samples that had similar features. …”
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MedFuseNet: fusing local and global deep feature representations with hybrid attention mechanisms for medical image segmentation by Ruiyuan Chen, Saiqi He, Junjie Xie, Tao Wang, Yingying Xu, Jiangxiong Fang, Xiaoming Zhao, Shiqing Zhang, Guoyu Wang, Hongsheng Lu, Zhaohui Yang

“…For feature fusion and enhancement, the designed hybrid attention mechanisms combine four different attention modules: (1) an atrous spatial pyramid pooling (ASPP) module for the CNN branch, (2) a cross attention module in the encoder for fusing local and global features, (3) an adaptive cross attention (ACA) module in skip connections for further performing fusion, and (4) a squeeze-and-excitation attention (SE-attention) module in the decoder for highlighting informative features. …”
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MSGEGA: Multiscale Gaussian Enhancement and Global-Aware Network for Infrared Small Target Detection by Yuyang Xi, Liuwei Zhang, Ying Jiang, Feng Qian, Fanjiao Tan, Qingyu Hou

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Lightweight Attention-Based CNN Architecture for CSI Feedback of RIS-Assisted MISO Systems by Anming Dong, Yupeng Xue, Sufang Li, Wendong Xu, Jiguo Yu

“…Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. …”
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Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection by Meng Jiao, Changyue Song, Xiaochen Xian, Shihao Yang, Feng Liu

“…Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. …”
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Detection of <i>Helicobacter pylori</i> Infection in Histopathological Gastric Biopsies Using Deep Learning Models by Rafael Parra-Medina, Carlos Zambrano-Betancourt, Sergio Peña-Rojas, Lina Quintero-Ortiz, Maria Victoria Caro, Ivan Romero, Javier Hernan Gil-Gómez, John Jaime Sprockel, Sandra Cancino, Andres Mosquera-Zamudio

“…The aim of the present article is to detect the presence of <i>HP</i> infection from our own institutional dataset of histopathological gastric biopsy samples using different pretrained and recognized DCNN and AutoML approaches. …”
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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…In addition, the multi-source feature fusion demonstrates strong adaptability across different growth environments. In Xinxiang, the R2 ranges from 0.76 to 0.88, the RMSE ranges from 0.35 to 0.50, and the rRMSE ranges from 8.73 % to 12.40 %. …”
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Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis by Kai E. Yang, Lucas A. Tarr, Matthias Rempel, S. Curt Dodds, Sarah A. Jaeggli, Peter Sadowski, Thomas A. Schad, Ian Cunnyngham, Jiayi Liu, Yannik Glaser, Xudong Sun

“…Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been explored; each case covers six solar-hours, totaling 109 TB in data volume. …”
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Comparative Analysis of Vision Transformers and CNN Models for Driver Fatigue Classification by Fadhlan Hafizhelmi Kamaru Zaman, Kok Mun Ng, Syahrul Afzal Che Abdullah

“… This study provides a comprehensive evaluation of Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver fatigue classification, a critical issue in road safety. …”
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